Temporal networks are essential for modeling and understanding systems whose behavior varies in time, from social interactions to biological systems. Often, however, real-world data are prohibitively expensive to collect in a large scale or unshareable due to privacy concerns. A promising way to bypass the problem consists in generating arbitrarily large and anonymized synthetic graphs with the properties of real-world networks, namely `surrogate networks'. Until now, the generation of realistic surrogate temporal networks has remained an open problem, due to the difficulty of capturing both the temporal and topological properties of the input network, as well as their correlations, in a scalable model. Here, we propose a novel and simple method for generating surrogate temporal networks. Our method decomposes the input network into star-like structures evolving in time. Then those structures are used as building blocks to generate a surrogate temporal network. Our model vastly outperforms current methods across multiple examples of temporal networks in terms of both topological and dynamical similarity. We further show that beyond generating realistic interaction patterns, our method is able to capture intrinsic temporal periodicity of temporal networks, all with an execution time lower than competing methods by multiple orders of magnitude. The simplicity of our algorithm makes it easily interpretable, extendable and algorithmically scalable.

翻译：时序网络对于建模和理解随时间变化的系统行为至关重要，从社交互动到生物系统均不例外。然而，真实世界的数据往往因大规模采集成本过高或隐私问题无法共享。一种有前景的解决途径是生成具有真实网络特性的任意大规模匿名合成图，即"替代网络"。由于难以在可扩展模型中同时捕捉输入网络的时序与拓扑特性及其相关性，生成逼真的替代时序网络至今仍是开放问题。本文提出一种新颖且简单的方法来生成替代时序网络：该方法将输入网络分解为随时间演化的星型结构，再以这些结构为构建块生成替代时序网络。在多个时序网络实例的拓扑与动力学相似性评估中，我们的模型显著优于现有方法。进一步研究表明，该方法不仅能生成真实的交互模式，还能捕捉时序网络内在的周期性规律，且执行时间比竞品低数个数量级。算法的简洁性使其易于解释、扩展且具备算法可扩展性。